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What is Honeywell UniSim Design Suite?
Honeywell's UniSim® Design Suite is a versatile software platform used for process modeling and simulation, enabling engineers to create both steady-state and dynamic models that improve plant design, optimization, and troubleshooting capabilities. This comprehensive suite features specific tools designed to support sustainability efforts, including the capture of carbon emissions and the creation of green hydrogen, which enhances operational efficiency and aligns with broader business goals. Moreover, UniSim models can act as digital twins, allowing for the monitoring of plant performance, predicting operational changes, and enhancing safety, production, and profitability across various sectors. By providing these capabilities, the tool equips engineers with the insights necessary to make decisions that not only advance operational efficiency but also foster a more sustainable industrial environment. Ultimately, the UniSim® Design Suite stands as a critical asset in driving innovation and promoting sustainability within the industry.
What is COLUMBO?
A universal multivariable optimizer, designed for closed-loop systems, aims to improve the performance and quality of Model Predictive Control (MPC) systems. This optimizer harnesses data from Excel files derived from Dynamic Matrix Control (DMC) by Aspen Tech, Robust Model Predictive Control Technology (RMPCT) from Honeywell, or Predict Pro from Emerson, facilitating the development and fine-tuning of precise models for various multivariable-controller variable (MV-CV) pairs. This cutting-edge optimization solution does away with the need for step tests that are usually required by Aspen Tech and Honeywell, functioning entirely in the time domain to maintain user-friendliness, compactness, and efficiency. As Model Predictive Controls (MPC) often involve numerous dynamic models—sometimes tens or even hundreds—there is a significant risk of utilizing incorrect models. Inaccurate dynamic models in MPCs can introduce bias, which appears as model prediction errors, leading to inconsistencies between expected signals and actual sensor measurements. COLUMBO emerges as a robust tool to bolster the precision of Model Predictive Control (MPC) models, effectively leveraging either open-loop or fully closed-loop data to guarantee peak performance. By tackling the risks associated with errors in dynamic models, COLUMBO not only enhances the reliability of the control system but also contributes to a more efficient operational framework. Ultimately, its implementation is expected to yield substantial advancements in control system effectiveness across various applications.
Integrations Supported
Aspen DMC3
Microsoft Excel
API Availability
Has API
API Availability
Has API
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
Honeywell International
Company Location
United States
Company Website
process.honeywell.com/us/en/products/industrial-software/process-optimization/unisim-design-suite
Company Facts
Organization Name
PiControl Solutions
Company Location
United States
Company Website
www.picontrolsolutions.com/products/columbo/
Categories and Features
Simulation
1D Simulation
3D Modeling
3D Simulation
Agent-Based Modeling
Continuous Modeling
Design Analysis
Direct Manipulation
Discrete Event Modeling
Dynamic Modeling
Graphical Modeling
Industry Specific Database
Monte Carlo Simulation
Motion Modeling
Presentation Tools
Stochastic Modeling
Turbulence Modeling